code: def output_results(
      original_source: torch.Tensor,
      predicted_source: torch.Tensor,
      source: str,
  ):
      print(
          "SDR score is:",
          separation.bss_eval_sources(
              original_source.detach().numpy(),
              predicted_source.detach().numpy(),
          )[0].mean(),
      )
      plot_spectrogram(
          stft(predicted_source)[0], f"Spectrogram - {source}"
      )
      return Audio(predicted_source, rate=sample_rate)
  
file_path: audio/examples/tutorials/hybrid_demucs_tutorial.py
imports: import torch
  import torchaudio
  import matplotlib.pyplot
  from IPython.display import Audio
  from mir_eval import separation
  from torchaudio.pipelines import HDEMUCS_HIGH_MUSDB_PLUS
  from torchaudio.utils import download_asset
  from torchaudio.transforms import Fade
label_desc: Use image node_id 3 for calling a global function with example usage: output_results(original_source, predicted_source, source) and returns: Audio